|제목||[학술세미나] [학과세미나] 3월 9일(금) 11시 학과세미나 안내|
|내용||[학과세미나] 3월 9일(금) 11시 학과세미나 안내
▪제목 : Functional Horseshoe Priors for Subspace Shrinkage
▪연사 : 신민석 (The Data Science Initiative, Harvard University)
▪일시 : 2018년 3월 9일(금) AM 11:00 – 12:00
▪장소 : 25동 405호
We introduce a new shrinkage prior on function spaces, called the functional horseshoe prior (fHS), that encourages shrinkage towards parametric classes of functions. Unlike other shrinkage priors for parametric models, the fHS shrinkage acts on the shape of the function rather than inducing sparsity on model parameters. We study the ecacy of the proposed approach by showing an adaptive posterior concentration property on the function. We also demonstrate consistency of the model selection procedure that thresholds the shrinkage parameter of the functional horseshoe prior.We apply the fHS prior to nonparametric additive models and compare its performance with procedures based on the standard horseshoe prior and several penalized likelihood approaches. We find that the new procedure achieves smaller estimation error and more accurate model selection than other procedures in several simulated and real examples.
세미나 안내_180309_신민석.hwp [25.5KB]